Optimal Pheromone Utilization
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Optimal Pheromone Utilization (in ANTS)
Optimal Pheromone UtilizationRoman KecherJoint work with: Yehuda Afek and Moshe Sulamy
Tel-Aviv UniversityAnts Nearby Treasure SearchInfinite gridk antsInitially at the originFood at distance D
Ants have to findthe food
Optimal run-time: (D + D2/k)[Feinerman, Korman, Lotker, Sereni, 2012]
WENS
Dk
(0,0)Ants are mobile agents essentially
Well be using Manhattan distance
PheromonesAnts emit pheromones[Lenzen, Radeva, 2013]Or notAnd sense them
No other communication
Biological resourceGoal: minimize pheromone count
WENS
Other communication models exist: constant size messages
Ants can not communicate in other forms, they cant even sense other ants
We would like to use minimal number of pheromones and still solve the problem effecientlyGround RulesEvery ant runs same algorithm (locally)With same initial state
Only uniform algorithms,ants have no knowledge of:k, total number of antsD, distance to the food
Synchronous ModelRounds:all ants move onceper round
WENS
Synchronous ModelRounds:all ants move onceper round
Assumption: antemission scheme[Emek, Langner, Uitto, Wattenhofer, 2013]At most one ant isemitted in each round
WENS
Note that there might be rounds where no ant is emitted; we only require constant (unknown) delays between emissionsAsynchronous Model
WENS
Adversary repeatedlyschedules one ant
Test&Set:Sense and emit a pheromone is oneatomic step
Definition of Rounds:Round ends when everyant took at least one stepOnly for (time) complexityAnts ModelsFSM: Finite State MachinesConstant size memory
TM: Turing MachinesUnlimited memory
Both deterministicResultsLower BoundAlgorithmFSM(Deterministic)TM(Deterministic)Previously known: O(D2) pheromones [Lenzen, Radeva, 2013]ResultsLower BoundAlgorithmFSM(Deterministic)(D) pheromonesto find the foodO(D) pheromonesO(D + D2/k) timeTM(Deterministic)Previously known: O(D2) pheromones [Lenzen, Radeva, 2013]ResultsLower BoundAlgorithmFSM(Deterministic)(D) pheromonesto find the foodO(D) pheromonesO(D + D2/k) timeTM(Deterministic)Results hold for Synchronous and Asynchronous modelsPreviously known: O(D2) pheromones [Lenzen, Radeva, 2013]Do not forget to say a word on Turing Machines, and that we only see half the story now, but will see the rest soonLayersDefinition: layer LAll grid cells at distance L from origin
WENSLThis is something we will need for the following slides
WENFSM Need (D) PheromonesAssume FSM withS statesUses o(D) pheromones
S+1 consecutivepheromone-free layers exist
Path starts and ends in same stateInfinite loop
SPath contains no pheromones - verballyFSM Algorithms
WENS
Problem:FSM cant count
Solution:Use pheromonesas turning points
Similar to the idea of guides[Emek, Langner, Uitto, Wattenhofer, 2013]
Note the duplicated walks on each cell
Basically, there are two problems:Enumerate the whole space this is shown herePrevent ants from doing duplicated work this is shown in the sync and async variantsAsynchronous FSM Algorithm
WENS
Mark E, S, W, N
Explore from NN never longer than E, S or W
Test&Set preventsmultiple ants fromexploring same layer
Note the duplicated walks on each cellSynchronous FSM Algorithm
WENSEmission schemebreaks initialsymmetry
But what happensif two ants collide?
Veteran ants behavedifferently thanNewbie ants
VeteranNewbie
This time we only explore every other layer (save duplication), to prevent misinterpretation of extra pheromoneResultsLower BoundAlgorithmFSM(Deterministic)(D) pheromonesto find the foodO(D) pheromonesO(D + D2/k) timeTM(Deterministic)Results hold for Synchronous and Asynchronous modelsPreviously known: O(D2) pheromones [Lenzen, Radeva, 2013]ResultsLower BoundAlgorithmFSM(Deterministic)(D) pheromonesto find the foodO(D) pheromonesO(D + D2/k) timeTM(Deterministic)(k) pheromonesfor optimal run timeO(k) pheromonesO(D + D2/k) timeResults hold for Synchronous and Asynchronous modelsPreviously known: O(D2) pheromones [Lenzen, Radeva, 2013]Async TM Need (k) Pheromones
WENSAssume oneant does not emitpheromones
Async TM Need (k) Pheromones
WENSAssume oneant does not emitpheromones
Consider samescheduling butwith extra antsAll new ants followthat one ant
Runtime remains thesame (but more ants)
Sync TM Need (k) Pheromones
WENSEmit one antUntil all pheromonesare placedEmit second antUntil all pheromonesare placedContinueDelay is constant
If no new pheromonesare placed, all followingants behave the same
Asynchronous TM Algorithm
WENSTM can count!Use pheromonesto assign IDs to ants
Static partitionExplore layersL = ID (mod Total)Occasionally updateestimated Total
Also works for thesynchronous model
ID = 1ID = 212111111122222222222222211111111111111111111111122222222222222222222222222222222
ThanksQuestions?